Tuesday, 24 March 2009

More on Charting Collective Knowledge

How do universities prepare students for jobs that don’t yet exist? Harold Jarche recently presented this grand challenge to Education and Industry. This challenge is highly relevant to the development and growth of talent for industry and for the economy.

My colleagues Anoush Margaryan , Colin Milligan and I (Allison Littlejohn ) have been extending our ideas on how we might rise to this challenge. We have been developing the concept of Charting Collective Knowledge by carrying out empirical research and debating ideas with a number of colleagues, including Karen Smith and Isobel Falconer from the Caledonian Academy.

Society, the workplace and knowledge itself is rapidly changing, bringing about the emergence of new economic paradigms. Production is no longer in the hands of organisations. In the information age knowledge generation is an increasingly significant means of production with ownership in the hands of individuals.
It is difficult predict what new roles will emerge over the next decade, never mind by the end of the century. However, individuals will increasingly be expected to be in control of their own knowledge, work and learning.

Collective learning and self-regulated learning (SRL) gaining importance due to global societal transformations which create new demands for learning for work (Jakupec et al, 2000). Education and training has lagged behind socio-economic demands, increasing the gap between education and work (Reynolds et al, 2001). Around the globe governments are trying to ensure that employers have a role in future development of Higher Education and the importance of bringing closer the worlds of work and learning has been emphasised internationally (ETUC, 2006; EU, 2005; EU 2006; EU 2007).

Across Europe there have been initiatives to enhance this transition from education to work. through our “employability skills” or “”graduate attributes” agenda. There has been debate as to how these skills and attributes can be defined, taught and assessed. These debates often overlook one key point: that the development of expertise is not fixed in time, but requires ongoing refinement. Therefore standalone skills development cannot provide a solution. Development of an individual’s expertise involves a change in mindset with commitment to lifelong , self-regulated learning based around self efficacy and motivation. And its essential that individuals can develop skills in networking, and collaboration to help navigate the many transitions they will encounter throughout their career . In a new society where knowledge is generated openly and collaboratively, people require new skills and literacies enabling them to learn as an individual, drawing from collective intelligence. Development of these skills has to be integrated within approaches to learning.

The transition to work from education is problematic partly because although self-regulation is required in both contexts, the nature and goals of learning are very different. In higher education learning is a goal in itself, while in the workplace it is a means to and end and a by-product of carrying out work tasks. Consequently in the workplace the underlying motives, alignment of learning with work goals, assessment and forms of support are not familiar to new graduates. This poses difficulties for graduates as they try to orientate themselves in the workplace and enhance their self-regulating skills (Candy, 1991).


Immersing new employees in Collective learning may alieviate these problems. Collective learning processes makes use of collective knowledge and intelligence within and beyond the organisation. In drawing upon such collective knowledge, the individual develops a network of relationships with colleagues, connects with appropriate resources, and actively contributes knowledge and experiences. Through network interactions, knowledge and structures emerge from which the individual, in turn, benefits in his or her learning process. There has to be, therefore, a strong link between the tools supporting individual self-direction and the collective knowledge residing within groups, communities and networks within which collective learning processes emerge.

Collective learning draws from and contributes to collective knowledge. Collective learning is based upon a metaphor of the ‘wisdom of the crowds’ (Surowiecki, 2004), the idea that large groups of connected people are better able than an elite few to produce knowledge to solve problems and foster innovation.

In recent years knowledge networks, based on Web2.0 technologies, have extended groups learning to learning communities. However some learning environments still confine learning groups within ‘walled gardens’ protected by passwords. Collective learning extends beyond the limitations of networks capitalising on all knowledge distributed on the web - in humans, their actions, their networks and their interactions through machines.

In collective learning, individuals consume, connect and contribute knowledge. In consuming knowledge, these individuals need to be able to identify and source knowledge residing within the collective. To enable them to find relevant knowledge, the knowledge base must be transparent and accessible. Connecting knowledge requires that different resources and components (both those residing in systems and in individuals) can be combined efficiently. Contributing knowledge, through creating and sharing, is a vital condition for collective learning. Generating new skills, solutions, processes and feeding these back into the collective is an essential component.

These three components of collective learning represent a set of intertwined activities rather than discrete steps. They are not novel, having been emphasised in many modern pedagogic approaches (Dron, 2007; Siemens, 2006). However, what is missing is, firstly, an understanding of how these components should be linked in a way that supports individuals in accomplishing their work and learning goals in their contexts. Secondly, an understanding and solutions to creating synergies between learning and cognition in humans and machines that allow systems to identify learning requirements, intelligently monitor progress and exploit learners actions to help them learn better.

While navigating collective intelligence the learner needs guidance in how to make sense of the fragments of knowledge she will encounter. This allows her to tap into whatever is important. We propose the concept of ‘charting’ as a mechanism for this integration.


Charting is an approach (a collection of behaviours) that helps individuals navigate their learning and development goals. Other approaches currently exist such as Personal Development Planning, portfolios and so on. The problem with these approaches is that they are not dynamic and are individually driven rather than tapping into the collective.

Imagine if a new employee setting her learning goals could dynamically look up someone else’s plan and see how they reached their learning goals. Charting allows this. It is both individually focussed AND collaborative allowing individuals use other peoples’ knowledge while setting personal development plans.

Charting is a powerful concept that can support faster acquisition of knowledge, competences and skills thereby accelerating time to competence. Charting is a process whereby an individual determines and executes their individual learning paths. In doing this, individuals assess their current competence and set precise learning and developmental goals. Although this process is individually driven, it is not an individualistic learning process, since it takes place within the socio-cultural context of the workplace. In charting a learning path ideally suited to their needs, learners take advantage of collective knowledge, seeing how others with similar goals achieved them and their reflections on the process. The approach requires learners to both create and share knowledge, to allow others to build on their experience and to contribute to the collective knowledge. Charting also connects learners to others with similar goals and development needs, creating networks of learners who may support each other in learning and work. In doing this they should be able to use their own tools, networks, communities, and resources alongside those of the collective.

Charting can be supported by a ‘toolbox’ to support individuals consuming, connecting and contributing knowledge.

Fig2: charting collective knowledge

Imagine a new employee who sets her own goals and plans the sorts of activities required to achieve these goals. Imagine she is a process chemist tasked with finding a coolant substance for drilling in a new type of substrate. To achieve this goals she will set herself a real life task of testing coolants. To select the right sort of coolant and understand how to proceed she may make use of a variety of sources of information, knowledge and learning such as formal learning resources, stored information, recommended resources, case studies and so on (see Figure 2).

These resources are not always easy to find, since they are distributed. Also they may use language unfamiliar to the employee, leading to organisational and cultural issues. To help the employee overcome these issues she may tap into the expertise of others within her organisation who can guide her, for example her manager, team, peers and so on (Figure 2)
This goal is easiest if its part of the working culture of the organisation and if it is recognised and rewarded. The employee will consume knowledge from these resources and services. She may draw on technology tools to make recommendations as to which resources to select and how to make use of them, based on the actions of others. Connecting with others within and outside the organisation can help her make best use of what’s available to carry out her work and learning tasks and achieve her goal of finding an optimal coolant for drilling. In carrying out these tasks she will, in turn, contribute back to all of these resources and services. She will carry out her work and learning tasks alongside others within her peer group who are setting their own learning and development goals and activities. Not only will this improve her learning productivity, but it will enrich the collective and she will draw upon the knowledge and actions of others as she does so. Consequently all individuals will contribute knowledge, implicitly and explicitly, that can be used by the collective as they move towards achieving their goals. Charting occurs at a level above connection, consumption and contribution. The key to charting is the ability of the employee to define her learning goals.

Goal setting

To maximise the speed with which this employee will learn, the ideal situation is where everything this individual does in her daily working life will contribute towards her learning. In other words, an employee carrying out a job will learn through her normal work tasks. Learning is aligned with her work tasks, knowledge creation and knowledge sharing. This direct route to achieving leaning goals allows for maximum learning productivity.

In reality not everything an employee or learner does will directly contribute to achieving her learning goals, particularly in workplace settings where learning goals are loosely defined and not as linear as in formal education. Learning goals may alter over time. An employee, whether expert or novice, may not be able to predict from the outset where her emerging goals will lead her.

Charting may help the learner relate where she is to where she wants to be and will help her find how to get there by recommending existing trails used by previous learners following a similar route. Charting provides the learner opportunity to dynamically interact with her goals and personal development. Charting allows the learner to make use of people and resources to fine tune her choices at any point. We cannot exactly define charting prior to looking at existing behaviours. And that is what we are currently doing in partnership with Shell.


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